Transactions on Emerging Telecommunications Technologies最新文献

Correction to “Lattice Homomorphic Assisted Privacy Preserving Electronic Health Records Data Transmission in Internet of Medical Things Using Blockchain”

IF 2.5 4区计算机科学

Transactions on Emerging Telecommunications Technologies Pub Date : 2025-04-07 DOI: 10.1002/ett.70124

引用次数: 0

Secure Transactions in IoT Network Using PINN-Based Intrusion Detection System and HDPoA Blockchain Protocol

IF 2.5 4区计算机科学

Transactions on Emerging Telecommunications Technologies Pub Date : 2025-04-07 DOI: 10.1002/ett.70116

Sourav Mishra, Vijay Kumar Chaurasiya

{"title":"Secure Transactions in IoT Network Using PINN-Based Intrusion Detection System and HDPoA Blockchain Protocol","authors":"Sourav Mishra, Vijay Kumar Chaurasiya","doi":"10.1002/ett.70116","DOIUrl":"https://doi.org/10.1002/ett.70116","url":null,"abstract":"<div>\u0000 \u0000 <p>IoT is a rapidly developing technology with a wealth of creative application possibilities. However, IoT wireless sensor networks are vulnerable to Denial of Service (DoS) attacks due to their insecure nature. Although network integrity and security have been ensured through the use of distributed ledger and blockchain technologies, privacy preservation concerns frequently arise with traditional approaches. So, deep learning-based Physics-informed neural networks (PINN) and Honesty-based Distributed Proof-of-Authority (HDPoA) are developed to enhance transaction security and detect intrusions. Initially, the mobile nodes were deployed in different regions to gather transactions and an intrusion detection system to analyze attacks. First, the Intrusion Detection System (IDS) uses a deep learning approach for detecting the intrusion in the network. For that, the collected data from the deployed nodes are pre-processed using Variational auto-encoder and min-max normalization to standardize input dataset values. Then the features are selected using wild horse optimization and classified using PINN to predict data attack or non-attack. After that, Homomorphic variable tag generation is used for normal transactions with multiple copies in the same document, which are then converted into hash values using the Keccak hashing function. The miner validates transactions based on rank-based priority. Honesty-based Distributed Proof-of-Authority (HDPoA) was used for network security, making it suitable for deployment in blockchain-based IoT applications. The proposed deep learning-based PINN classifier reached 97.2% accuracy and 96.52% specificity. Homomorphic variable (HV) tag generation takes 0.4 s, while the Keccak algorithm takes 0.3 s for hash generation, and the HDPoA protocol has 420 s for block generation time.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 4","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143793292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Insider Attack Monitoring and Secured Data Transfer Using KPCC and BiO-ELSTM in Cloud Computing

IF 2.5 4区计算机科学

Transactions on Emerging Telecommunications Technologies Pub Date : 2025-04-07 DOI: 10.1002/ett.70095

Rama Krishna Mani Kanta Yalla, Vijaykumar Mamidala, Akhil Raj Gaius Yallamelli, Mohanarangan Veerappermal Devarajan, Thirusubramanian Ganesan, Aceng Sambas

{"title":"Insider Attack Monitoring and Secured Data Transfer Using KPCC and BiO-ELSTM in Cloud Computing","authors":"Rama Krishna Mani Kanta Yalla, Vijaykumar Mamidala, Akhil Raj Gaius Yallamelli, Mohanarangan Veerappermal Devarajan, Thirusubramanian Ganesan, Aceng Sambas","doi":"10.1002/ett.70095","DOIUrl":"https://doi.org/10.1002/ett.70095","url":null,"abstract":"<div>\u0000 \u0000 <p>Handling of User's data effectively in the cloud has become a crucial task. Monitoring insider attacks in the cloud and securely transferring cookie files have not been concentrated in the existing works. Insider threats pose significant risks to data security in cloud environments, particularly during data transfers between public and private/hybrid clouds. Existing methodologies often fail to adequately monitor these threats and secure data, leading to vulnerabilities such as cookie attacks and data integrity issues. This paper proposes a novel framework that integrates the Cyclic permutation-based Hirose-Streebog (CH-Streebog) algorithm for secure logins and hashcode verification, alongside the KPCC method for securing cookie files. Additionally, the WS-Fuzzy method is employed for optimal virtual machine selection, and the BiO-ELSTM classifier is utilized for effective insider threat monitoring, aiming to reduce false alarm rates. The proposed framework demonstrates impressive performance metrics, achieving a precision of 98.95%, recall of 98.02%, f-measure of 98.48%, accuracy of 98.26%, and specificity of 98.78%. These results indicate a significant improvement in insider threat detection and secure data handling in cloud environments compared to existing methods.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 4","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143793291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Efficient Resource Allocation in Digital Twin-Assisted Mobile Edge Computing Network

IF 2.5 4区计算机科学

Transactions on Emerging Telecommunications Technologies Pub Date : 2025-04-02 DOI: 10.1002/ett.70108

Ilsa Rameen, Mudassar Ali, Muhammad Imran, Muhammad Naeem

{"title":"Efficient Resource Allocation in Digital Twin-Assisted Mobile Edge Computing Network","authors":"Ilsa Rameen, Mudassar Ali, Muhammad Imran, Muhammad Naeem","doi":"10.1002/ett.70108","DOIUrl":"https://doi.org/10.1002/ett.70108","url":null,"abstract":"<div>\u0000 \u0000 <p>Considering the rapid growth in user count and increasing demand for higher data rates, we need to optimize network strategies to accommodate more users and increase the network throughput. Therefore, this work aims to accommodate maximum IoT nodes and increase the network's throughput simultaneously by optimizing the IoT node association and power allocation in the digital twin (DT)-assisted mobile edge computing (MEC) network. The DT of each cloudlet is considered here which helps to optimize the power allocation and IoT node association. The DT technology helps make more accurate and optimized decisions in the MEC network by creating real-time digital representations of physical objects. This work formulates the optimization problem as a mixed integer nonlinear programming problem. To solve the proposed problem, the outer approximation algorithm is used due to its lesser complexity. The proposed algorithm's convergence, effectiveness, and lesser complexity leads to <span></span><math></math>-optimal solution = <span></span><math></math>, achieved using standard problem solvers. The simulation results in terms of associated IoT nodes and the network's throughput demonstrate the effectiveness of the proposed approach.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 4","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143749636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Energy Efficient Dual Index Multicarrier M-Ary Cyclic Shifted Chaotic Vector Shift Keying System

IF 2.5 4区计算机科学

Transactions on Emerging Telecommunications Technologies Pub Date : 2025-04-01 DOI: 10.1002/ett.70123

G. Bavatharani, T. Laxmikandan, T. Manimekalai

{"title":"Energy Efficient Dual Index Multicarrier M-Ary Cyclic Shifted Chaotic Vector Shift Keying System","authors":"G. Bavatharani, T. Laxmikandan, T. Manimekalai","doi":"10.1002/ett.70123","DOIUrl":"https://doi.org/10.1002/ett.70123","url":null,"abstract":"<div>\u0000 \u0000 <p>Chaotic signals are highly suitable for spread spectrum communication due to their inherent anti-jamming and anti-interference properties. Chaos-based multicarrier systems meet the increasing demands for high spectral efficiency, energy efficiency, secure, and reliable communication. However, the transmission of reference signals in these systems negatively impacts both spectral and energy efficiency. In this work, we enhance the energy efficiency of chaos-based multicarrier systems by employing index modulation with dual indexing on carriers, combined with cyclic-shifted chaotic signals. This approach not only improves energy efficiency but also reduces hardware complexity by utilizing a subset of subcarriers. Semi-analytical expressions for bit error rate (BER) over AWGN (additive white Gaussian noise) and multipath fading channels have been derived and validated through simulations. Comparative analysis reveals that the proposed system achieves significant improvement in BER performance and offers gain in energy efficiency up to a 2.5 dB over existing chaotic multicarrier systems and reduced hardware complexity in addition.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 4","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143741401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Secure Multi-Model Biometrics Using Deep Learning Model Based-Optimal Hybrid Pattern by the Heuristic Approach

IF 2.5 4区计算机科学

Transactions on Emerging Telecommunications Technologies Pub Date : 2025-04-01 DOI: 10.1002/ett.70106

Samatha J, Madhavi Gudavalli

引用次数: 0

Survey of Blockchain-Based Industrial Metaverse in SMMEs

IF 2.5 4区计算机科学

Transactions on Emerging Telecommunications Technologies Pub Date : 2025-04-01 DOI: 10.1002/ett.70109

Dianwei Wang, Junbiao Wang, Aijun Yan, Taotao Li, Yi Zhou, Qinqrui Tu, Jingfeng Zhu, Yejun Yang

{"title":"Survey of Blockchain-Based Industrial Metaverse in SMMEs","authors":"Dianwei Wang, Junbiao Wang, Aijun Yan, Taotao Li, Yi Zhou, Qinqrui Tu, Jingfeng Zhu, Yejun Yang","doi":"10.1002/ett.70109","DOIUrl":"https://doi.org/10.1002/ett.70109","url":null,"abstract":"<div>\u0000 \u0000 <p>The convergence of blockchain technology and the industrial metaverse is poised to revolutionize digital transformation in small- and medium-sized manufacturing enterprises (SMMEs), addressing their unique challenges and unlocking new opportunities for innovation and competitiveness. This survey explores the fundamental concepts, state-of-the-art advancements, and practical applications of blockchain and industrial metaverse technologies, focusing on their synergistic potential to reshape supply chain management, enhance production efficiency, and drive intelligent manufacturing. Key blockchain features such as decentralization, distributed ledgers, and smart contracts are analyzed alongside metaverse technologies like digital twins, virtual reality, and augmented reality, showcasing their transformative impact on data security, product traceability, and operational efficiency. Case studies and industry insights highlight the practical integration of these technologies in SMMEs, demonstrating their ability to overcome resource constraints and align with strategic goals. The survey also addresses the major challenges—technological, financial, and infrastructural—faced by SMMEs in digital transformation, providing actionable strategies and policy recommendations. By examining the interplay between blockchain and the industrial metaverse, this paper underscores the emerging trends and future trajectories of these technologies, offering a comprehensive roadmap for leveraging their potential to achieve sustainable growth and innovation in the manufacturing sector.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 4","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143749684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An IoMT-Enabled Surgical Monitoring System Utilizing Robotics and AI With E2ARiA-RESNET-50 and MI-KMEANS

IF 2.5 4区计算机科学

Transactions on Emerging Telecommunications Technologies Pub Date : 2025-03-31 DOI: 10.1002/ett.70082

Dinesh Kumar Reddy Basani, Basava Ramanjaneyulu Gudivaka, Rajya Lakshmi Gudivaka, Raj Kumar Gudivaka, Sri Harsha Grandhi, Faheem khan

{"title":"An IoMT-Enabled Surgical Monitoring System Utilizing Robotics and AI With E2ARiA-RESNET-50 and MI-KMEANS","authors":"Dinesh Kumar Reddy Basani, Basava Ramanjaneyulu Gudivaka, Rajya Lakshmi Gudivaka, Raj Kumar Gudivaka, Sri Harsha Grandhi, Faheem khan","doi":"10.1002/ett.70082","DOIUrl":"https://doi.org/10.1002/ett.70082","url":null,"abstract":"<div>\u0000 \u0000 <p>Robotic automated Surgery uses robots to assist with surgeries, making procedures more precise and recovery faster. It is popular in healthcare because it enables surgeries with smaller incisions, leading to quicker healing and shorter hospital stays. However, existing research often neglects the implementation of strong safety measures and fail-safes in robotic surgical systems. Therefore, this paper presents a robotic-based AI framework for monitoring the surgical phase, utilizing E2ARiA-RESNET-50 AND MI-KMEANS. Initially, the input video is preprocessed, including frame conversion, key frame extraction, blur and distortion removal using AKRDF with sharpening. Next, data are balanced using SMOTE. Super-resolution is then performed using PWLC-SRGAN, followed by variability analysis in tissue appearance using MI-KMEANS and patch extraction. In the meantime, from super-resolution, segmentation is done by ROI-WA, followed by masking. Then, features are extracted from both patch-extracted and masked images. Finally, these extracted features are classified using E2ARiA-RESNET-50 for monitoring. The experimental results revealed that the proposed model reached a high accuracy of 98.625%, outperforming traditional methods.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 4","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143741690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Simultaneous Vortex- and Non-Vortex-Based Transmission

IF 2.5 4区计算机科学

Transactions on Emerging Telecommunications Technologies Pub Date : 2025-03-29 DOI: 10.1002/ett.70122

Man Hee Lee, Hye Yeong Lee, Soo Young Shin

引用次数: 0

Enhancing Electric Vehicle Battery Performance and Safety Through IoT and Machine Learning: A Fire Prevention Approach

IF 2.5 4区计算机科学

Transactions on Emerging Telecommunications Technologies Pub Date : 2025-03-29 DOI: 10.1002/ett.70112

Uma S, R. Eswari

{"title":"Enhancing Electric Vehicle Battery Performance and Safety Through IoT and Machine Learning: A Fire Prevention Approach","authors":"Uma S, R. Eswari","doi":"10.1002/ett.70112","DOIUrl":"https://doi.org/10.1002/ett.70112","url":null,"abstract":"<div>\u0000 \u0000 <p>This research presents a comprehensive assessment and comparison of various battery technologies employed in EVs, including lithium-ion, nickel-metal hydride, solid-state, lithium iron phosphate, and sodium-ion batteries. A novel approach integrating IoT sensors and machine learning is proposed to monitor and analyze battery performance under real-world driving conditions, with a strong emphasis on fire prevention and safety. Through an extensive literature review, the inherent characteristics, advantages, and limitations of each battery type are explored. IoT sensors deployed in EVs can collect real-time data on important factors, such as voltage, current, temperature, and state of charge (SoC). Machine learning algorithms process this data to realize degradation patterns, optimize battery management strategies, and enhance charging protocols. By leveraging data-driven insights, this research aims to improve battery efficiency, extend lifespan, and mitigate fire hazards. The proposed approach achieves a battery performance prediction accuracy of 99.4%, reduces fire risk by 72%, and improves overall battery efficiency by 18.6% compared to conventional methods. Ultimately, the findings will contribute to the development of safer and more sustainable EV battery technologies, shaping the future of eco-friendly mobility.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 4","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143726867","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0